Lightweight and Optimized Multi-Label Fruit Image Classification: A Combined Approach of Knowledge Distillation and Image Enhancement

Abstract

In our research, we aimed to address the shortcomings of traditional fruit image classification models, which struggle with inconsistent lighting, complex backgrounds, and high computational demands. To overcome these challenges, we developed a novel multi-label classification method incorporating advanced image preprocessing techniques, such as Contrast Limited Adaptive Histogram Equalization and the Gray World algorithm, which enhance image quality and color balance. Utilizing lightweight encoder–decoder architectures, specifically MobileNet, DenseNet, and EfficientNet, optimized with an Asymmetric Binary Cross-Entropy Loss function, we improved model performance in handling diverse sample difficulties. Furthermore, Multi-Label Knowledge Distillation (MLKD) was implemented to transfer knowledge from large, complex teacher models to smaller, efficient student models, thereby reducing computational complexity without compromising accuracy. Experimental results on the DeepFruit dataset, which includes 21,122 images of 20 fruit categories, demonstrated that our method achieved a peak mean Average Precision (mAP) of 90.2% using EfficientNet-B3, with a computational cost of 7.9 GFLOPs. Ablation studies confirmed that the integration of image preprocessing, optimized loss functions, and knowledge distillation significantly enhances performance compared to the baseline models. This innovative method offers a practical solution for real-time fruit classification on resource-constrained devices, thereby supporting advancements in smart agriculture and the food industry.